Land Cover Model Research Articles

How does imperviousness develop and affect runoff generation in an urbanizing watershed?

Imperviousness associated with urbanization remains one of the biggest challenges in sustainable urban design. The replacement of forests, marshlands, buffers, and wetlands with impervious surfaces, strongly influences hydrological processes in urbanizing areas. This study analyzed the contribution of four constructed surfaces types – roofs, yards, roads, and an international airport – to surface runoff within a 21 km 2 watershed, and presents the development over five decades (1977−2030). The land-cover model, used to assess watershed imperviousness in 2030, utilized coefficients between impervious areas generating surface runoff and the floor area, developed during the study. The conducted imperviousness analysis allowed the evaluation of land-use development impacts on the stream network, and the identification of hydrologically active areas for urban planning and stormwater management. Research revealed the importance of yard imperviousness related to suburban residential housing for stormwater runoff generation, and the impacts of transport-related imperviousness on stormwater runoff.

Epistemic uncertainty in predicting shorebird biogeography affected by sea-level rise

Accurate spatio-temporal predictions of land-cover are fundamentally important for assessing geomorphological and ecological patterns and processes. This study quantifies the epistemic uncertainty in the species distribution modeling, which is generated by spatio-temporal gaps between the biogeographical data, model selection and model complexity. Epistemic uncertainty is generally given by the sum of subjective and objective uncertainty. The subjective uncertainty generated by the modeler-choice in the manipulation of the environmental variables was analyzed. The Snowy Plover in Florida (Charadrius alexandrinus nivosus, SP), a residential shorebird whose geographic range is extended along the Panhandle-Big Bend-Peninsula Gulf coast was considered as case-study. The first fundamental step for studying the species distribution and how it will be affected by climate change is to obtain an accurate description of the shorebird coastal habitat. The land-cover was translated into ecosystem classes using a land-cover model that predicts the evolution of coastal ecosystems affected by sea-level rise scenarios. The best land-cover map decreased the objective uncertainty (intrinsically present in data or models) in representing the spatial structure of the coastal ecosystem, reduced the temporal gaps with the occurrence data, and diminished the subjective uncertainty due to the conversion from land-cover to model-classes. Multimodeling was performed to reduce the uncertainty in the prediction of the species distribution related to model uncertainty. The best representation of the species distribution was performed by MaxEnt. The area under the receiver operating characteristic curve (AUC), the omission/commission test, the similarity index of the response curves, and the jackknife test were used simultaneously as indicators of the predictability of each species distribution model. The availability of updated high-resolution biogeoclimatological data was proven to be necessary in order to properly predict the species ranges for conservation purposes.

A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

Changes in land use, land cover, disturbance regimes, and land management have considerable influence on carbon and greenhouse gas (GHG) fluxes within ecosystems. Through targeted land-use and land-management activities, ecosystems can be managed to enhance carbon sequestration and mitigate fluxes of other GHGs. National-scale, comprehensive analyses of carbon sequestration potential by ecosystem are needed, with a consistent, nationally applicable land-use and land-cover (LULC) modeling framework a key component of such analyses. The U.S. Geological Survey has initiated a project to analyze current and projected future GHG fluxes by ecosystem and quantify potential mitigation strategies. We have developed a unique LULC modeling framework to support this work. Downscaled scenarios consistent with IPCC Special Report on Emissions Scenarios (SRES) were constructed for U.S. ecoregions, and the FORE-SCE model was used to spatially map the scenarios. Results for a prototype demonstrate our ability to model LULC change and inform a biogeochemical modeling framework for analysis of subsequent GHG fluxes. The methodology was then successfully used to model LULC change for four IPCC SRES scenarios for an ecoregion in the Great Plains. The scenario-based LULC projections are now being used to analyze potential GHG impacts of LULC change across the U.S.

Modelling the impact of land-cover change on potential soil loss in the Taita Hills, Kenya, between 1987 and 2003 using remote-sensing and geospatial data *

In sub-Saharan Africa, natural vegetation is being transformed into agricultural lands at a fast rate, endangering ecosystem services and increasing soil-loss potential, which may trigger land degradation. For the Taita Hills study area in Kenya, multi-temporal land-cover models of 1987, 1999 and 2003, derived from Satellite Pour l'Observation de la Terre (SPOT) imagery using a multi-scale segmentation/object relationship modelling (MSS/ORM) methodology and a rainfall layer, a digital elevation model (DEM) and a digital soil map were applied to model potential soil loss. Population growth in the area has led to a shortage of agricultural land and movement of people to the lowlands, evidenced by a 39% (9.3 km2) increase in croplands from 30% to 41% of the study area during the research time frame. Expansion took place mostly in surrounding foothills and lowlands, at the expense of natural shrubland and grassland, but also occurred in the hills. Universal soil-loss equation (USLE) model results showed a 60% (4 km2) increase in the area of very high potential soil loss, from 7% of the study area in 1987 to 12% in 2003, due mainly to very high soil-loss potential in croplands. Whilst the area of croplands as a whole increased, the relative proportion of very high soil-loss potential in croplands remained 20%, both in 1987 and in 2003, indicating that newly cleared agricultural lands with vulnerable soils are the most at-risk areas.

Habitat associations of the marsh rice rat (Oryzomys palustris) in freshwater wetlands of southern Illinois

The marsh rice rat (Oryzomys palustris) is a semiaquatic rodent occurring in wetland habitats throughout the southeastern United States and along the Atlantic Coast. A lack of understanding of its ecology and distribution in inland parts of its range limits our ability to assess the species’ status and needs. We trapped rice rats at random and previously occupied, wetland-dominated sites in 5 southern Illinois watersheds during 2007–2009 to determine key variables affecting habitat occupancy by the species. We detected rice rats within 3 of 5 watersheds, 16 of 48 sites, and at 5 new locations. Most rice rats were captured in permanent or semipermanent emergent wetlands (n 5 89; 46.3% of total captures) or roadside ditches in wetland patches (n 5 73; 38.0%). Habitat associations determined using logistic regression and occupancy modeling provided similar results. Percent herbaceous cover and percent visual obstruction (0.0–0.5 m) were the most important microhabitat variables positively influencing rice rat occurrence. In areas surrounding wetlands, the proportion composed of upland grass was the best predictive variable of rice rat occurrence among landcover models. Estimates of daily detection probability were high (0.44–0.87). The likelihood of occupancy increased with the proportion of upland grass cover adjacent to wetland complex and percent herbaceous cover at the microhabitat scale. Inland metapopulations of rice rats are clustered throughout the southeastern United States in appropriate wetland complexes. Construction, restoration, and protection of emergent wetlands, and consideration of connectivity and adjacent grasslands, should benefit rice rat populations.

Landcover predicts isolation in Ambystoma salamanders across region and species

Anthropogenic modification of the landscape can have important consequences for dispersal of terrestrial organisms. If certain landcover types consistently act as barriers to movement, their occurrence could serve as a proxy for population isolation. We tested the fit of a general a priori landcover model on genetically-inferred isolation in ambystomatid salamander species. The landcover model included terms for pond size, geographic isolation, and amount of surrounding agriculture. In all cases, agriculture was associated with increased population isolation, while deciduous forest was almost always associated with decreased isolation. The full model described over 70% of the variation in a genetically-inferred measure of isolation in the system for which it was developed, and 30–45% of variation in novel systems. We suggest that landscape analyses can serve as a proxy for population isolation in ambystomatid salamanders. Our findings suggest that preserving well-forested regions without substantial agriculture will protect salamanders through increased population connectivity. These results have important implications for conservation, as genetic analyses are costly and time-consuming, and decisions to purchase or preserve land must often occur quickly.

Using the FORE-SCE model to project land-cover change in the southeastern United States

A wide variety of ecological applications require spatially explicit current and projected land-use and land-cover data. The southeastern United States has experienced massive land-use change since European settlement and continues to experience extremely high rates of forest cutting, significant urban development, and changes in agricultural land use. Forest-cover patterns and structure are projected to change dramatically in the southeastern United States in the next 50 years due to population growth and demand for wood products [Wear, D.N., Greis, J.G. (Eds.), 2002. Southern Forest Resource Assessment. General Technical Report SRS-53. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, 635 pp]. Along with our climate partners, we are examining the potential effects of southeastern U.S. land-cover change on regional climate. The U.S. Geological Survey (USGS) Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States, providing ecoregion-by-ecoregion estimates of the rates of change, descriptive transition matrices, and changes in landscape metrics. The FORecasting SCEnarios of future land-cover (FORE-SCE) model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land-cover change through 2050 for the southeastern United States. Prescriptions for future proportions of land cover for this application were provided by ecoregion-based extrapolations of historical change. Logistic regression was used to develop relationships between suspected drivers of land-cover change and land cover, resulting in the development of probability-of-occurrence surfaces for each unique land-cover type. Forest stand age was initially established with Forest Inventory and Analysis (FIA) data and tracked through model iterations. The spatial allocation procedure placed patches of new land cover on the landscape until the scenario prescriptions were met, using measured Land Cover Trends data to guide patch characteristics and the probability surfaces to guide placement. The approach provides an efficient method for extrapolating historical land-cover trends and is amenable to the incorporation of more detailed and focused studies for the establishment of scenario prescriptions.

Application of GIS and logistic regression to fossil pollen data in modelling present and past spatial distribution of the Colombian savanna

Climate changes affect the abundance, geographic extent, and floral composition of vegetation, which are reflected in the pollen rain. Sediment cores taken from lakes and peat bogs can be analysed for their pollen content. The fossil pollen records provide information on the temporal changes in climate and palaeo-environments. Although the complexity of the variables influencing vegetation distribution requires a multi-dimensional approach, only a few research projects have used GIS to analyse pollen data. This paper presents a new approach to palynological data analysis by combining GIS and spatial modelling. Eastern Colombia was chosen as a study area owing to the migration of the forest–savanna boundary since the last glacial maximum, and the availability of pollen records. Logistic regression has been used to identify the climatic variables that determine the distribution of savanna and forest in eastern Colombia. These variables were used to create a predictive land-cover model, which was subsequently implemented into a GIS to perform spatial analysis on the results. The palynological data from the study area were incorporated into the GIS. Reconstructed maps of past vegetation distribution by interpolation showed a new approach of regional multi-site data synthesis related to climatic parameters. The logistic regression model resulted in a map with 85.7% predictive accuracy, which is considered useful for the reconstruction of future and past land-cover distributions. The suitability of palynological GIS application depends on the number of pollen sites, the distribution of the pollen sites over the area of interest, and the degree of overlap of the age ranges of the pollen records.

Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: A case study of the Southwest Regional Gap Analysis Project (SWReGAP)

Land-cover mapping efforts within the USGS Gap Analysis Program have traditionally been state-centered; each state having the responsibility of implementing a project design for the geographic area within their state boundaries. The Southwest Regional Gap Analysis Project (SWReGAP) was the first formal GAP project designed at a regional, multi-state scale. The project area comprises the southwestern states of Arizona, Colorado, Nevada, New Mexico, and Utah. The land-cover map/dataset was generated using regionally consistent geospatial data (Landsat ETM+ imagery (1999–2001) and DEM derivatives), similar field data collection protocols, a standardized land-cover legend, and a common modeling approach (decision tree classifier). Partitioning of mapping responsibilities amongst the five collaborating states was organized around ecoregion-based “mapping zones”. Over the course of 2 1/ 2 field seasons approximately 93,000 reference samples were collected directly, or obtained from other contemporary projects, for the land-cover modeling effort. The final map was made public in 2004 and contains 125 land-cover classes. An internal validation of 85 of the classes, representing 91% of the land area was performed. Agreement between withheld samples and the validated dataset was 61% (KHAT = .60, n = 17,030). This paper presents an overview of the methodologies used to create the regional land-cover dataset and highlights issues associated with large-area mapping within a coordinated, multi-institutional management framework.

Fish-habitat modeling for gap analysis to conserve the endangered Topeka shiner (Notropis topeka)

Decision support tools that predict fish distribution over broad spatial scales are needed to assist in planning watershed management and endangered species recovery. We developed a geographical information system model with multivariate logistic regression to rank valley segments for probable occurrence of the endangered Topeka shiner (Notropis topeka) using stream condition variables (stream size, groundwater potential, channel slope, streamflow, network position) and land-cover variables (percent pasture, percent trees) in streams characteristic of the North American Great Plains. The stream condition and land-cover models correctly classified 89% and 68% of outcomes (i.e., presence or absence), respectively. Field tests of maps of predicted species distribution resulted in more species occurrences than expected in valley segments classified as high potential for presence and less than expected in low-potential valley segments. Gaps between high-priority segments and protected land parcels were found in all basins. In 37 basins with Topeka shiners, protected land coverage was <1% in 17 basins, 1–5% in 10 basins, and 5–21.8% in 10 basins. Conservation activities in gaps are long-term conservation measures, but maps of predicted species distribution have many immediate applications.

Defining the importance of including transient ecosystem responses to simulate C-cycle dynamics in a global change model

Summary The ability of plant species to migrate is one of the critical issues in assessing accurately the future response of the terrestrial biosphere to climate change. This ability is confined by both natural and human-induced changes in land cover. In this paper we present land-cover and Carbon (C) cycle models designed to simulate the biospheric consequences of different types of land-cover changes. These models, imbedded in the larger integrated assessment model IMAGE 2, were used to demonstrate the importance of considering spatial aspects for global C-cycle modelling. A gradual-migration, an unlimited-migration and a no-migration case were compared to show the range of possible consequences. Major differences between these cases were simulated for land-cover patterns and the carbon budget. A large geographical variation in the biospheric response was also simulated. The strongest response was simulated in high-latitude regions, especially for the migration cases in which land-cover changes were permitted. In low-latitudes regions the differences between the migration cases were smaller, mainly due to the effects of land-use changes. The geographical variation among, and the different responses, the migration cases clearly demonstrate how essential it is to assess biospheric responses to climate change and land use simultaneously. Moreover, it also shows the urgent need for enhanced understanding of spatial and temporal dynamics of the biospheric responses.

A model analysis of the terrestrial vegetation model of image 2.0 for Costa Rica

The Terrestrial Vegetation Model (TVM) of the Integrated Model to Assess the Greenhouse Effect ( image 2.0) determines potential land cover for natural ecosystems and potential productivity for agrosystems. A model analysis of this TVM using Monte Carlo simulations is described for the eighteen Costa Rican (0.5° × 0.5° latitude-longitude) image 2.0 grid cells. It is demonstrated that the TVM is not overly sensitive to climate input variability. Much observed output variability is related to model parameters. These model parameter related effects can strongly determine the intra/inter grid variability for Costa Rican potential yields. The model analysis indicates that more model refinement is required to make any effort to improve data quality effective. It is therefore proposed to remove as many model criteria as possible or to replace them by more gradual criteria based on crop ecophysiological characteristics. A first tentative comparison of maize, rice and pulses yield potentials and their distribution in Costa Rica during 1973 and 1984, suggests that the assumption that the two are related, as assumed in the Land Cover Model (LCM) in image 2.0, has some validity.